Articles | Volume 11, issue 1
https://doi.org/10.5194/gmd-11-77-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-77-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
09 Jan 2018
Development and technical paper |
| 09 Jan 2018
| 09 Jan 2018
The Cloud Feedback Model Intercomparison Project Observational Simulator Package: Version 2
Dustin J. Swales
CORRESPONDING AUTHOR
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, USA
NOAA/Earth System Research Laboratory, Boulder, Colorado, USA
Robert Pincus
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, USA
NOAA/Earth System Research Laboratory, Boulder, Colorado, USA
Alejandro Bodas-Salcedo
Met Office Hadley Centre, Exeter, UK
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Cited articles
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufrense, J. L., Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V.:
COSP: satellite simulation software for model assessment,
B. Am. Meteorol. Soc.,
92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011.
Chepfer, H., Bony, S., Winker, D., Chiriaco, M., Dufresne, J.-L., and Seze, G.:
Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model,
Geophys. Res. Lett.,
35, L15704, https://doi.org/10.1029/2008GL034207, 2008.
Chepfer, H., Noel, V., Winker, D., and Chiriaco, M.:
Where and when will we observe cloud changes due to climate warming?,
Geophys. Res. Lett.,
41, 8387–8395, https://doi.org/10.1002/2014GL061792, 2014.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.:
Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization,
Geosci. Model Dev.,
9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Haynes, J. M., Marchand, R., Luo, Z., Bodas-Salcedo, A., and Stephens, G. L.:
A multipurpose radar simulation package: QuickBeam,
B. Am. Meteorol. Soc.,
88, 1723–1727, https://doi.org/10.1175/BAMS-88-11-1723, 2007.
Kay, J. E., Hillman, B. R., Klein, S. A., Zhang, Y., Medeiros, B. P., Pincus, R., Gettelman, A., Eaton, B., Boyle, J., Marchand, R., and Ackerman, T. P.:
Exposing global cloud biases in the Community Atmosphere Model (CAM) using satellite observations and their corresponding instrument simulators,
J. Climate,
25, 5190–5207, https://doi.org/10.1175/JCLI-D-11-00469.1, 2012.
Klein, S. A. and Jakob, C.:
Validation and sensitivities of frontal clouds simulated by the ECMWF model,
Mon. Weather Rev.,
127, 2514–2531, https://doi.org/10.1175/1520-0493(1999)127<2514:VASOFC>2.0.CO;2, 1999.
Klein, S. A., Zhang, Y., Zelinka, M. D., Pincus, R., Boyle, J., and Gleckler, P. J.:
Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator,
J. Geophys. Res.,
118, 1329–1342, https://doi.org/10.1002/jgrd.50141, 2013.
Marchand, R. and Ackerman, T. P.:
An analysis of cloud cover in Multiscale Modeling Framework Global Climate Model Simulations using 4 and 1 km horizontal grids,
J. Geophys. Res.,
115, D16207, https://doi.org/10.1029/2009JD013423, 2010.
Nam, C. C. W. and Quaas, J.:
Evaluation of clouds and precipitation in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data,
J. Climate, 25, 4975–4992,
https://doi.org/10.1175/JCLI-D-11-00347.1, 2012.
Nam, C., Bony, S., Dufresne, J.-L., and Chepfer, H.:
The “too few, too bright” tropical low-cloud problem in CMIP5 models,
Geophys. Res. Lett.,
39, L21801, https://doi.org/10.1029/2012GL053421, 2012.
Pincus, R., Barker, H. W., and Morcrette, J.-J.:
A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields,
J. Geophys. Res.,
108, 4376, https://doi.org/10.1029/2002JD003322, 2003.
Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., and Hofmann, R. J. P.:
Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the limits of instrument simulators,
J. Climate,
25, 4699–4720, https://doi.org/10.1175/JCLI-D-11-00267.1, 2012.
Suzuki, K., Golaz, J.-C., and Stephens, G. L.:
Evaluating cloud tuning in a climate model with satellite observations,
Geophys. Res. Lett.,
40, 4464–4468, https://doi.org/10.1002/grl.50874, 2013.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.:
An overview of CMIP5 and the experiment design,
B. Am. Meteorol. Soc.,
93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S., Chadwick, R., Chepfer, H., Douville, H., Good, P., Kay, J. E., Klein, S. A., Marchand, R., Medeiros, B., Siebesma, A. P., Skinner, C. B., Stevens, B., Tselioudis, G., Tsushima, Y., and Watanabe, M.:
The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6,
Geosci. Model Dev.,
10, 359–384, https://doi.org/10.5194/gmd-10-359-2017, 2017.
Webb, M. J., Senior, C., Bony, S., and Morcrette, J.-J.:
Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models,
Clim. Dynam.,
17, 905–922, https://doi.org/10.1007/s003820100157, 2001.
Zelinka, M. D., Klein, S. A., and Hartmann, D. L.:
Computing and partitioning cloud feedbacks using cloud property histograms. Part I: Cloud radiative kernels,
J. Climate,
25, 3715–3735, https://doi.org/10.1175/JCLI-D-11-00248.1, 2012.
Zelinka, M. D., Klein, S. A., Taylor, K. E., Andrews, T., Webb, M. J., Gregory, J. M., and Forster, P. M.:
Contributions of different cloud types to feedbacks and rapid adjustments in CMIP5,
J. Climate,
26, 5007–5027, https://doi.org/10.1175/JCLI-D-12-00555.1, 2013.
Short summary
This paper introduces a new version of diagnostic software (COSP2) intended to facilitate more straightforward comparisons between climate models and observational cloud datasets. This version allows users to more closely incorporate their own models assumptions within COSP, while also being computationally more efficient and straightforward for users to extend and build upon.
This paper introduces a new version of diagnostic software (COSP2) intended to facilitate more...
